Aim: To analyze performance.

Starting with October 2013:

##    Month Month Desc Received Resolved Reopen Backlog
## 1      1   Oct 2013      103      104      5       0
## 2      2   Nov 2013       99       93      0       6
## 3      3   Dec 2013       79       65      3      14
## 4      4   Jan 2014       78       63      2      15
## 5      5   Feb 2014      120      113      1       7
## 6      6   Mar 2014      144      143      3       1
## 7      7   Apr 2014      157      151      5       6
## 8      8   May 2014      127      119      1       8
## 9      9   Jun 2014      139      134      0       5
## 10    10   Jul 2014      151      145      4       6
## 11    11   Aug 2014      119      131      1       0
## 12    12  Sept 2014      109      107      0       2
## 13    13   Oct 2014      138      131      2       7
## 14    14   Nov 2014       99      107      0       0
## 15    15   Dec 2014       83       90      2       0
## 16    16   Jan 2015       58       54      1       4
## 17    17   Feb 2015      112      108      0       4
## 18    18   Mar 2015      118      126      2       0

Plotting graph:

library(ggplot2)
gL1 <- ggplot(data, aes(x=Month, y=NumberOfCases)) + geom_line(aes(y=Received, colour="Received"))+labs(title="Performance Analysis") + geom_line(aes(y=Resolved, colour="Resolved")) + geom_line(aes(y=Reopen, colour="Reopen")) + scale_colour_manual("", breaks=c("Received","Resolved","Reopen"), values=c("purple","maroon","orange"))
gL1

plot of chunk performance

Load Analysis

Plotting graph:

library(ggplot2)
gL1 <- ggplot(data, aes(x=Month, y=NumberOfCases)) + geom_line(aes(y=Received, colour="Received"))+labs(title="Load Analysis") + geom_line(aes(y=Resolved, colour="Resolved")) + geom_line(aes(y=Backlog, colour="Backlog")) + scale_colour_manual("", breaks=c("Received","Resolved","Backlog"), values=c("purple","maroon","orange"))
gL1

plot of chunk load

Predicting Performance for the Next Month

For Received cases:

library(lattice)
library(caret)
meanReceived <- mean(data$Received)
meanReceived
## [1] 112.9
stdev <- sd (data$Received)
stdev
## [1] 27.4
n <- 18
meanReceived + c(-1,1) * qt(.975, n-1) * stdev / sqrt(n)
## [1]  99.32 126.57

Therefore, there is a 95% chance that the number of Received cases for the next month will be between 99.3 and 126.6.

For Resolved cases:

meanResolved <- mean(data$Resolved)
meanResolved
## [1] 110.2
stdevRes <- sd (data$Resolved)
stdevRes
## [1] 28.61
meanResolved + c(-1,1) * qt(.975, n-1) * stdevRes / sqrt(n)
## [1]  95.99 124.45

Therefore, there is a 95% chance that the number of Resolved cases for the next month will be between 95.9 and 124.45.

For Reopened cases:

meanReopen <- mean(data$Reopen)
meanReopen
## [1] 1.778
stdevReo <- sd (data$Reopen)
stdevReo
## [1] 1.665
meanReopen + c(-1,1) * qt(.975, n-1) * stdevReo / sqrt(n)
## [1] 0.9499 2.6056

Therefore, there is a 95% chance that the number of Reopened cases for the next month will be between 0.9 and 2.6.